Search Results (327)

Salt stress is one of the main abiotic stresses that restrict crop production. Melatonin (MT), a signal molecule widely present in plants, plays an important role in regulating abiotic stress response. In this study, celery seedlings were used as experimental materials, and the control, salt stress, and exogenous MT treatment groups under salt stress were set up. Through phenotypic, physiological index determination, transcriptome sequencing, and expression analysis, the alleviation effects of MT on salt stress were comprehensively investigated. The results showed that exogenous MT treatment significantly reduced seedling growth inhibition caused by salt stress. Physiological measurements showed that MT significantly reduced malondialdehyde content, increased the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), promoted the accumulation of free proline and soluble protein, and increased photosynthetic parameters such as chlorophyll, ΦPSII, Fv/Fm, and ETR. Transcriptome analysis showed that MT regulates the expression of several genes associated with carbon metabolism, including β-amylase gene (AgBAM), sucrose-degrading enzyme genes (AgSUS, AgINV), and glucose synthesis-related genes (AgAG, AgEGLC, AgBGLU). The results of qRT-PCR verification were highly consistent with the transcriptome sequencing data, revealing that MT synergistically regulates starch and sucrose metabolic pathways, and effectively alleviates the damage of celery seedlings under salt stress at the molecular level. In summary, exogenous MT significantly improved the salt tolerance of celery by enhancing antioxidant capacity, maintaining photosynthetic function, promoting the accumulation of osmotic adjustment substances, and synergistically regulating carbon metabolism-related pathways. The concentration of 200 μM was identified as optimal, based on its most pronounced alleviating effects across the physiological parameters measured. This study provides an important theoretical basis for utilizing MT to enhance plant salt resistance. Full article

Background: The golden apple snail (Pomacea canaliculata), an invasive species originating from South America, has inflicted considerable agricultural and ecological harm in non-native habitats. While the molluscicide niclosamide is currently effective against P. canaliculata, its prolonged use raises environmental concerns and the risk of resistance development. Results: BAM 15 possesses strong molluscicidal activity against P. canaliculata, with 72 h LC₅₀ values of 0.4564 mg/L for adults (shell height: 20–25 mm), 0.3352 mg/L for subadults (10–15 mm), and 0.1142 mg/L for juveniles (2–3 mm). Metabolomic and proteomic profiling revealed that the altered metabolites and proteins both converged on energy metabolism and oxidative stress. Experimental validation revealed that BAM15 collapsed the mitochondrial membrane potential, drove MDA and H₂O₂ upward while depleting NADPH, boosted CAT, SOD and GPX activities, yet suppressed GR, and ultimately inflicted overt damage in the head-foot tissue of P. canaliculata. Conclusions: Our findings reveal that BAM 15 operates via a three-stage mechanism: (1) it disrupts membrane potential (ΔΨm) and impairs ATP production, severely disturbing energy metabolism; (2) energy deficits stimulate excessive electron transport chain activity, generating reactive oxygen species (ROS) and initiating oxidative stress; (3) persistent metabolic imbalance and oxidative damage culminate in extensive tissue injury. These results identify BAM 15 as a promising candidate for molluscicide development. Full article

Cosmetic wipes are made for multiple functions, baby care, hand washing, feminine and personal cleansing, removing makeup, and applying products such as deodorants and sunless tanners among other uses. Despite the presence of preservatives, cosmetic wipes can become contaminated during processing steps and usage, which may lead to skin infections and other health issues for consumers. No validated method exists for the microbiological testing of cosmetic wipes. The goal of this study was to develop and validate a specific sample preparation method for the quantitative detection of microorganisms in cosmetic wipes for inclusion in the FDA Biological Analytical Manual (BAM). Ten wipe types differing in their composition and preservative combinations were inoculated with Bacillus cereus spore suspensions at three concentration levels and aged for 14 days. Three extraction methods were compared: mBAM1g (reference method using 1 g samples), mBAMww (whole wipe method based on BAM Chapter 23), and ISOww (whole wipe method based on ISO method without Tween 80). For commercial wipes, mBAMww and ISOww, using whole wipes, performed similarly (p ≥ 0.05) or significantly better (p < 0.05) than mBAM1g. For laboratory-made wipes, 1 g samples showed higher recovery rates than whole wipes, likely due to cell loss during aging. Inoculation method and preservatives affect microbial distribution, survival, and recovery rates. T80 may have a positive effect on the recovery of B. cereus from wipes. This study recommends mBAMww for the microbiological analysis of cosmetic wipes. Full article

Buildings account for nearly 32% of global energy consumption and serve as key demand-side flexibility resources in power systems with high renewable penetration. However, their utilization is constrained by the lack of an integrated framework that can jointly quantify energy-adjustable margin (BAM) and response duration (RD) under realistic operational and thermal comfort constraints. This study presents a coupled data–physical simulation framework integrating a Particle Swarm Optimization–Long Short-Term Memory–Random Forest (PSO-LSTM-RF) hybrid load forecasting model with EnergyPlus(24.1.0)-based building simulation. The PSO-LSTM-RF model achieves high-accuracy short-term load prediction, with an average R² of 0.985 and mean absolute percentage errors of 1.92–5.75%. Predicted load profiles are mapped to physically consistent baseline and demand-response scenarios using a similar-day matching mechanism, enabling joint quantification of BAM and RD under explicit thermal comfort constraints. Case studies on offices, shopping malls, and hotels reveal significant heterogeneity: hotels exhibit the largest BAM (up to 579.27 kWh) and longest RD (up to 135 min), shopping malls maintain stable high flexibility, and offices show moderate BAM with minimal operational disruption. The framework establishes a closed-loop link between data-driven prediction and physics-based simulation, providing interpretable flexibility indicators to support demand-response planning, virtual power plant aggregation, and coordinated optimization of source–grid–load interactions. Full article

In this study, a comprehensive analysis of the fixed/preassigned-time synchronization of a class of quaternion-valued BAM (QBAM) neural networks with stochastic and impulsive effects is conducted. Unlike previous analysis methods, our method features a direct analysis approach. First, to clarify the combined impact of impulsive and stochastic phenomena on synchronization behavior, we establish a QBAM neural network system incorporating stochastic and impulsive effects. Notably, differing from prior relevant studies, we assume that the activation function is discontinuous, thereby enhancing the practical relevance of this research. Second, leveraging the quaternion-valued sign function and its properties, we implement impulsive control via the direct analysis method to achieve Fixed/Predefined-Time synchronization of the considered system. Finally, numerical simulations are performed to verify the ability of the theoretical analysis and the proposed control protocol to realize synchronization under impulsive and stochastic effects. Full article

As a task in the digital preservation of calligraphy stone inscriptions, an invaluable cultural heritage, style classification faces prominent challenges: insufficient feature representation of single-channel rubbings, and difficulties in effectively capturing the complex strokes and spatial layouts inherent to calligraphic works. To tackle these issues, an efficient deep learning model integrated with the dual-path attention mechanism of Bottleneck Attention Module (BAM) is proposed in this paper, which is designed to achieve accurate and efficient classification of calligraphy styles. With the lightweight network EfficientNetB2 as its backbone, this model innovatively integrates the BAM. It realizes the channel-spatial collaborative attention in calligraphy analysis, with the weight of stroke structure features increased to over 85%. Through the synergistic effect of channel attention and spatial attention, the model’s ability to extract stroke structure and spatial layout features from calligraphy images is significantly enhanced. The experimental results on the stratified sampling dataset show that the model achieves an accuracy of 98.44% on the test set, a confusion matrix recall rate of 94.80%, an F1-score of 0.9675, a precision of 0.8690, and a macro-averaged Area Under the Curve (AUC) value of 0.9694. To further validate the effectiveness of the BAM module and the necessity of its dual-path design, we conducted a systematic ablation experiment analysis. The experiment used EfficientNet-B2 as the baseline model and sequentially compared the contributions of different attention mechanisms. The experimental results show that the method proposed in this paper balances efficiency and performance, and holds practical significance in fields such as ancient book authentication and calligraphy research. Full article

Metal–Organic Frameworks (MOFs) have diverse applications due to their tunable porosity, large surface area, and diverse chemical functionalities. Among them, one of the most researched MOFs is MIL-101(Cr), which, in addition, is very stable in water. We have used a commercially available substance with approximately 300 nm large crystals for the preparation of a sensing nano-thin layer for the emerging water contaminant PFOS, due to its high selectivity towards this compound. Here, we have synthesized 20 nm sized crystals of MIL-101(Cr), which are among the smallest reported, and compared them to the same material with 300 nm sized crystals. The material was characterized by TEM and XPS. It was possible to prepare insoluble monolayers at the air–water interface (Langmuir films), which were characterized with film compression isotherms, Brewster angle microscopy, and surface potential measurements. The Langmuir–Blodgett (LB) method was used to deposit monolayers on Si wafers and 434 MHz Surface Acoustic Wave resonator simultaneously. The LB layers were very stable over time. The smaller-sized MIL-101 (Cr) crystals exhibit denser, more homogeneous water coverage and packing upon compression, with no observable 10–100 µm aggregates. LB monolayers from the 20 nm particles have approximately six times lower surface roughness. The LB monolayer is far from being smooth, but this will allow excellent access to the MOF pores by the tested analyte in a chemical sensing application. The lack of research on depositing presynthesized MOFs using probably the best method for nanoarchitectonics—the LB method—is addressed. The 20 nm sized MOF crystals are the smallest deposited by this method so far. Full article

Ensuring the structural integrity of road infrastructure is vital for transportation safety and long-term sustainability. This study presents a lightweight and accurate pavement crack detection framework named SpcNet, which integrates a Sparse Encoding Module, ConvNeXt V2-based decoder, and a Binary Attention Module (BAM) within an asymmetric encoder–decoder architecture. The proposed method first applies a random masking strategy to generate sparse pixel inputs and employs sparse convolution to enhance computational efficiency. A ConvNeXt V2 decoder with Global Response Normalization (GRN) and GELU activation further stabilizes feature extraction, while the BAM, in conjunction with Channel and Spatial Attention Bridge (CAB/SAB) modules, strengthens global dependency modeling and multi-scale feature fusion. Comprehensive experiments on four public datasets demonstrate that SpcNet achieves state-of-the-art performance with significantly fewer parameters and lower computational cost. On the Crack500 dataset, the method achieves a precision of 91.0%, recall of 85.1%, F1 score of 88.0%, and mIoU of 79.8%, surpassing existing deep-learning-based approaches. These results confirm that SpcNet effectively balances detection accuracy and efficiency, making it well-suited for real-world pavement condition monitoring. Full article

This paper investigates the existence and global exponential stability of almost periodic solutions in a class of impulsive Cohen–Grossberg-type bidirectional associative memory (BAM) neural networks with time-varying delays. Real neural systems often experience sudden perturbations and nonuniform temporal interactions, leading to complex oscillatory behaviors. To capture these effects, a new impulsive Cohen–Grossberg BAM model is developed that integrates both delays and impulsive influences within a unified framework. Using the theory of almost periodic functions, fixed point methods, and impulsive differential inequalities, new sufficient conditions are derived for the existence and stability of almost periodic solutions. A Lyapunov functional combined with a generalized Gronwall-type inequality provides rigorous global exponential stability criteria. Numerical simulations confirm the theoretical analysis. The results extend existing studies and offer new insights into how delay and impulsive factors jointly shape the stability and dynamics of hybrid neural systems, contributing to the design of robust and delay-tolerant neural architectures. Full article

This study addresses the preassigned-time (PDT) projective lag synchronization of octonion-valued BAM neural networks (OV-BAMNNs) through exponential quantized event-triggered control (ETC). First, an OV-BAMNN model incorporating discontinuous activation functions and time-varying delays is established. Subsequently, by introducing the octonion-valued sign function, several exponential quantized ETC schemes are designed, which employ solely a single exponential term while eliminating traditional linear and power-law components. Compared with conventional ETC designs, the proposed control schemes are simpler in form. Furthermore, within the framework of the non-separation method, several criteria for PDT projective lag synchronization are derived based on the Lyapunov method and Taylor expansion, proving that Zeno behavior is excluded. Finally, two simulation examples are given to verify the correctness of the theoretical results and to apply these results to image encryption. Full article

Gel materials are widely used in underground mining for air leakage sealing and coal spontaneous combustion prevention. In this study, a novel self-healing carboxymethyl cellulose-modified amphiphilic polymer hydrogel with fire prevention and extinguishing capabilities is synthesized through ionic crosslinking between CMC-graft-poly(AM-co-NaA-co-BAM) and aluminum citrate (AlCit). The copolymer is constructed by grafting sodium carboxymethyl cellulose (CMC) onto an amphiphilic polymer backbone composed of acrylamide (AM), sodium acrylate (NaA), and N-benzylacrylamide (BAM), forming a dual-network structure via hydrophobic association and hydrogen bonding. The carboxymethyl cellulose-modified amphiphilic polymer demonstrates optimal viscosity-enhancing performance at a CMC content of 7.5 wt%. CMC-graft-poly(AM-co-NaA-co-BAM) demonstrated superior temperature, shear, and salt resistant performance compared with poly(AM-co-NaA-co-BAM), poly(AM-co-NaA), and CMC polymers, as well as enhanced viscoelasticity and self-healing capability. When crosslinked with AlCit, CMC-graft-poly(AM-co-NaA-co-BAM)-AlCit gel demonstrated superior viscoelastic properties and self-healing capability, as well as thermal stability, which gave the superior fire prevention and extinguishing performance for charcoal in fire extinction tests. CMC-graft-poly(AM-co-NaA-co-BAM) has abundant cross-linking sites, which lead to accelerated gelation and improved mechanical strength, while the hydrophobic microdomains acted as physical cross-linking points that interconnected polymer chains into a three-dimensional network. The hydrophobic interactions within the hydrogel are dynamically reversible. This intrinsic property allows physical cross-links to spontaneously reassociate when fracture surfaces make contact. Consequently, the material exhibits autonomous self-healing. Full article

This paper investigates the fixed-time stabilization and synchronization of a class of memristor inertial BAM neural networks with mixed delays using a non-order reduction method. By constructing a Lyapunov function and leveraging novel fixed-time stability lemmas, we design an aperiodic switching controller that addresses the inflexibility of traditional periodic control in high-order systems. Theoretical analysis proves that the controller ensures system states converge to equilibrium within a fixed time, independent of initial conditions. The inclusion of mixed delays further enhances the model’s practicality. Notably, the proposed method is applied to secure communication, demonstrating its capability to protect information transmission in realworld scenarios. Numerical simulations validate the effectiveness of the approach, with secure communication experiments specifically confirming its encryption potential. This work bridges theoretical control design with critical cybersecurity applications. Full article

In the wild, snow is not merely additive noise; it is a non-stationary, semi-transparent veil whose spatial statistics vary with depth, illumination, and wind. Because conventional two-stage pipelines first detect a binary mask and then inpaint the occluded regions, any early mis-classification is irreversibly baked into the final result, leading to over-smoothed textures or ghosting artifacts. We propose BAT-Net, a Bidirectional Attention Transformer Network that frames desnowing as a coupled representation learning problem, jointly disentangling snow appearance and scene radiance in a single forward pass. Our core contributions are as follows: (1) A novel dual-decoder architecture where a background decoder and a snow decoder are coupled via a Bidirectional Attention Module (BAM). The BAM implements a continuous predict–verify–correct mechanism, allowing the background branch to dynamically accept, reject, or refine the snow branch’s occlusion hypotheses, dramatically reducing error accumulation. (2) A lightweight yet effective multi-scale feature fusion scheme comprising a Scale Conversion Module (SCM) and a Feature Aggregation Module (FAM), enabling the model to handle the large scale variance among snowflakes without a prohibitive computational cost. (3) The introduction of the FallingSnow dataset, curated to eliminate the label noise caused by irremovable ground snow in existing benchmarks, providing a cleaner benchmark for evaluating dynamic snow removal. Extensive experiments on synthetic and real-world datasets demonstrate that BAT-Net sets a new state of the art. It achieves a PSNR of 35.78 dB on the CSD dataset, outperforming the best prior model by 1.37 dB, and also achieves top results on SRRS (32.13 dB) and Snow100K (34.62 dB) datasets. The proposed method has significant practical applications in autonomous driving and surveillance systems, where accurate snow removal is crucial for maintaining visual clarity. Full article

This research examines the Lyapunov-based criterion for global asymptotic stability of Bidirectional Associative Memory (BAM) neural networks that have mixed-interval time-varying delays. Using a second-order reciprocally convex approach, this paper introduces a novel stability criterion for BAM neural networks with time delays. The literature has recently incorporated a few triple integral expressions in the Lyapunov–Krasovskii functional to lessen conservatism in the analysis of system stability with interval time-varying delays using a second-order reciprocally convex combination strategy. This research work establishes the negative definiteness of the Lyapunov–Krasovskii functional derivative and is formulated using Linear Matrix Inequalities (LMIs). The effectiveness of the proposed result is demonstrated through numerical examples. Full article

Since the 2000s, Burkina Faso has experienced a rapid mining expansion with more than one hundred sites established, leading to increased waste generation often discharged untreated into the environment. Assessing water quality in these areas is therefore critical to mitigate environmental degradation and public health risks. This study develops a site-specific water quality index (WQI) for a gold mining area in Bam Province, Burkina Faso, with the objective of improving pollution monitoring and management in relation to tailing dams. Surface and groundwater samples were collected between 2021 and 2024. Physico-chemical and bacteriological analyses of groundwater sources including wells, piezometers and boreholes revealed that several parameters such as pH, turbidity, nitrates, sulphates, total iron, aluminium, arsenic, cadmium, cyanide and total and faecal coliforms exceeded international drinking water standards. Geospatial techniques were employed to identify the main contamination sources: domestic wastewater, industrial and artisanal mining and agricultural runoff. The evolution of these parameters in relation to the dynamics of soil occupation and the influence of geological structure has enabled the distinction of key parameters associated with discharges. Although individual contaminant levels were mostly moderate, their combined effects pose a significant long-term risk to ecosystems and human health. The tailored WQI is suitable for both surface water and groundwater. It provides an integrated tool for classifying and monitoring water quality in mining environments, supporting evidence-based decision making in the management of tailing dams, environmental protection and public health. Full article